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Z.AI · HƯỚNG DẪN MODEL CHO HERMES

GLM-5-Turbo API cho Hermes Agent: công việc và giá

GLM-5-Turbo is designed for work that does not end after one answer. It is a strong fit for Hermes workflows that call tools, follow schedules, keep state, and continue through a long chain of actions.

Quy trình Hermes Agent kết nối tác vụ GLM, tệp, công cụ và bước kiểm tra của con người

Persistent workflow agent

These facts show where GLM-5-Turbo fits inside Hermes and which details change the cost and review path.

InputText and tool state
OutputText and tool calls
Best rolePersistent workflow Agent
ToolsMCP, scheduled jobs, external actions
Price noteBudget for every tool round, not only the first prompt
Typical jobOpenClaw, MCP, queues, and scheduled work
  • Hermes owns the clock, retries, and permissions; the model does not run a schedule by itself.
  • Long chains can spend more tokens than a one-shot answer.
  • Put an approval step before money, customer data, or production changes.
  • Save the task brief and the last successful output.
  • Set retry limits and a clear stop condition.
  • Log every tool call and its result.
  • Require approval for external side effects.
01

Use it for persistent work

Scheduled jobs, MCP calls, OpenClaw tasks, and workflows that need repeated tool use.

02

Do not use it for one line answers

A small text model is cheaper for short questions, classification, and simple extraction.

03

A simple GLM-5-Turbo routing rule

If a task must wait, retry, call MCP tools, or remember earlier results, use GLM-5-Turbo. If it ends after one response, use a smaller model.

Chọn vai trò theo công việc

Các model GLM có vai trò khác nhau. Hãy xác định việc cần làm trước khi định tuyến.

01

Scheduled research

Run a daily search, collect sources, compare changes, and send a short report.

02

MCP workflows

Discover allowed tools, call them in order, and keep the result in a structured format.

03

Operations automation

Check a queue, update records, and report failed steps without losing the task state.

04

Long content jobs

Move from outline to draft to review while keeping the original brief and constraints.

The job runs every dayGLM-5-Turbo with a saved brief

It can continue from yesterday’s result instead of starting over.

The job uses several MCP toolsGLM-5-Turbo as the worker

It can choose the next allowed tool and keep the chain readable.

The job is one short answerUse a smaller model

Continuity is not worth the extra cost for a one-turn task.

The model keeps the chain moving; Hermes keeps it safe.

Hermes controls permissions, schedules, retries, and approvals. GLM-5-Turbo handles instruction decomposition, tool calls, and progress across steps.

01 · Schedule

Hermes starts the task at the right time and loads the saved brief.

02 · Call tools

GLM-5-Turbo selects the next allowed tool and explains the expected result.

03 · Continue

Hermes stores outputs and lets the model continue instead of starting over.

04 · Approve

A person reviews actions that can change money, data, or production systems.

Quy trình Hermes Agent kết nối tác vụ GLM, tệp, công cụ và bước kiểm tra của con người
Quy trình nghiên cứu GLM từ tệp và biểu đồ đến báo cáo đã kiểm tra

A research team turns a daily market check into a controlled agent job

Hermes runs every morning. GLM-5-Turbo reads the saved research brief, calls the approved search and document tools, compares the new results with yesterday, and writes a short change report. If a source is missing or a tool fails, Hermes records the problem instead of hiding it. A human approves any recommendation before it is sent to customers.

Hermes làm gì trong ví dụ này

01Schedule

Hermes starts the market check every morning with the saved brief.

02Collect

GLM-5-Turbo calls the approved search and document tools.

03Compare

Hermes stores today’s findings and asks for changes against yesterday.

04Approve

A human reviews recommendations before they reach customers.

Kết quả

The team gets a repeatable daily report with a visible error log instead of a silent failed automation.

Người kiểm tra

Review failed tool calls, source quality, external messages, and any action that changes records or sends money.

Use Turbo when continuity is worth more than a cheap first answer.

A persistent workflow may call several tools and use more tokens than a single prompt. The value comes from keeping the chain coherent and reducing manual restarts. Use a cheaper model for the small steps that do not need planning.

Ví dụ chi phí đơn giản

A daily workflow with six tool calls costs more than a single prompt. It can still be cheaper for a team when the saved state removes manual restarts and repeated setup.

Use GLM-5-Turbo in Hermes

Select glm-5-turbo, give Hermes the required MCP or tool permissions, and keep an approval step before external side effects.

curl https://api.hermes-ai.net/api/v1/chat/completions \
  -H "Authorization: Bearer $HERMES_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "glm-5-turbo",
    "messages": [{"role":"user","content":"Run the approved task and return a short verification report."}]
  }'

Câu hỏi thường gặp

Is GLM-5-Turbo only for OpenClaw?

It was optimized for OpenClaw style tasks, but the same strengths help Hermes workflows that use tools, schedules, and long execution chains.

Does it support MCP?

The official documentation lists MCP support. Hermes should still restrict which tools the model can call.

Can it run a scheduled job by itself?

Hermes owns the schedule. The model performs the task after Hermes starts it.

How do I handle failed tools?

Save the error, retry only when safe, and ask the model to return the failed step in its final report.

Should I use it for simple chat?

Usually not. A smaller model is a better fit for short, low risk questions.

HERMES API · GLM-5-Turbo

Use GLM-5-Turbo in Hermes

Select glm-5-turbo, give Hermes the required MCP or tool permissions, and keep an approval step before external side effects.